Motivation: Named Entity Recognition (NER) is a key task to support
biomedical research. In Biomedical Named Entity Recognition (BioNER), obtaining
high-quality expert annotated data is laborious and expensive, leading to the
development of automatic approaches such as distant supervision. However,
manually and automatically generated data often suffer from the unlabeled
entity problem, whereby many entity annotations are missing, degrading the
performance of full annotation NER models. Results: To address this problem, we
systematically study the effectiveness of partial annotation learning methods
for biomedical entity recognition over different simulated scenarios of missing
entity annotations. Furthermore, we propose a TS-PubMedBERT-Partial-CRF partial
annotation learning model. We harmonize 15 biomedical NER corpora encompassing
five entity types to serve as a gold standard and compare against two commonly
used partial annotation learning models, BiLSTM-Partial-CRF and EER-PubMedBERT,
and the state-of-the-art full annotation learning BioNER model PubMedBERT
tagger. Results show that partial annotation learning-based methods can
effectively learn from biomedical corpora with missing entity annotations. Our
proposed model outperforms alternatives and, specifically, the PubMedBERT
tagger by 38% in F1-score under high missing entity rates. The recall of entity
mentions in our model is also competitive with the upper bound on the fully
annotated dataset.

本文探讨了应用部分标注学习方法在缺失生物医学命名实体注释的场景中进行实体识别的有效性，并提出了一个新的 TS-PubMedBERT-Partial-CRF 模型来提高实体识别性能。实验结果表明，该模型在高缺失实体比例下的 F1-score 比使用全标注学习模型 PubMedBERT Tagger 高 38％，同时其实体识别的召回率也达到了与全标注学习数据集上的上界相竞争的水平。

生物医学实体识别的部分注释学习

Partial Annotation Learning for Biomedical Entity Recognition

We study learning named entity recognizers in the presence of missing entity
annotations. We approach this setting as tagging with latent variables and
propose a novel loss, the Expected Entity Ratio, to learn models in the
presence of systematically missing tags. We show that our approach is both
theoretically sound and empirically useful. Experimentally, we find that it
meets or exceeds performance of strong and state-of-the-art baselines across a
variety of languages, annotation scenarios, and amounts of labeled data. In
particular, we find that it significantly outperforms the previous
state-of-the-art methods from Mayhew et al. (2019) and Li et al. (2021) by
+12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased
annotations, averaged across 7 datasets. We also show that, when combined with
our approach, a novel sparse annotation scheme outperforms exhaustive
annotation for modest annotation budgets.

研究利用潜在变量标注的缺失实体标注学习命名实体识别器的方法，提出了期望实体比率的新损失函数，并证明其在理论和实践上的有效性和优越性。